Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images

TL;DR

This paper introduces a novel multi-class point cloud completion network for automatic 3D cardiac anatomy reconstruction from cine MRI, improving accuracy and robustness over existing methods.

Contribution

The paper presents a new multi-class point cloud completion network that enhances 3D cardiac reconstruction accuracy and robustness from cine MRI data, outperforming benchmark models.

Findings

Chamfer distances below image resolution on synthetic data

32% reduction in Hausdorff distance compared to 3D U-Net

Accurate, topologically plausible heart meshes on UK Biobank data

Abstract

Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional (2D) slices of the underlying three-dimensional (3D) anatomy of the heart, thus limiting the understanding and analysis of both healthy and pathological cardiac morphology and physiology. In this paper, we propose a novel fully automatic surface reconstruction pipeline capable of reconstructing multi-class 3D cardiac anatomy meshes from raw cine MRI acquisitions. Its key component is a multi-class point cloud completion network (PCCN) capable of correcting both the sparsity and misalignment issues of the 3D reconstruction task in a unified model. We first evaluate the PCCN on a large synthetic dataset of biventricular anatomies and observe Chamfer distances between reconstructed and gold standard anatomies below or similar to the underlying image resolution for multiple levels of slice misalignment. Furthermore, we find a reduction in reconstruction error compared to a benchmark 3D U-Net by 32% and 24% in terms of Hausdorff distance and mean surface distance, respectively. We then apply the PCCN as part of our automated reconstruction pipeline to 1000 subjects from the UK Biobank study in a cross-domain transfer setting and demonstrate its ability to reconstruct accurate and topologically plausible biventricular heart meshes with clinical metrics comparable to the previous literature. Finally, we investigate the robustness of our proposed approach and observe its capacity to successfully handle multiple common outlier conditions.